U.S. patent number 10,353,023 [Application Number 15/102,288] was granted by the patent office on 2019-07-16 for calculating mri rf coil sensitivities using interpolation into an enlarged field of view.
This patent grant is currently assigned to KONINKLIJKE PHILIPS N.V.. The grantee listed for this patent is KONINKLIJKE PHILIPS N.V.. Invention is credited to Tim Nielsen.
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United States Patent |
10,353,023 |
Nielsen |
July 16, 2019 |
Calculating MRI RF coil sensitivities using interpolation into an
enlarged field of view
Abstract
A magnetic resonance imaging (MRI) system (100) has a radio
frequency system (114, 116, 120, 124, 126) for acquiring magnetic
resonance data (142, 144, 156). The radio frequency system includes
a coil (124) with multiple antenna elements (126). The MRI system
further includes a processor (133) for controlling the magnetic
resonance imaging system. Execution of instructions (140, 170, 172,
174) cause the processor to: acquire (200) calibration magnetic
resonance data (142) from a first field of view within the imaging
zone using the multiple antenna elements, calculate (202, 300, 302,
304, 400) modified magnetic resonance data (144) by interpolating
the calibration magnetic resonance data to a second field of view,
calculate (204, 500, 502, 504, 602) a coil sensitivity kernel (146)
by deconvolving the modified magnetic resonance data, and calculate
(206, 604, 610) a coil sensitivity (148) by transforming each coil
sensitivity kernel into image space. The second field of view
encompasses and is larger than the first field of view.
Inventors: |
Nielsen; Tim (Eindhoven,
NL) |
Applicant: |
Name |
City |
State |
Country |
Type |
KONINKLIJKE PHILIPS N.V. |
Eindhoven |
N/A |
NL |
|
|
Assignee: |
KONINKLIJKE PHILIPS N.V.
(Eindhoven, NL)
|
Family
ID: |
49765834 |
Appl.
No.: |
15/102,288 |
Filed: |
November 27, 2014 |
PCT
Filed: |
November 27, 2014 |
PCT No.: |
PCT/EP2014/075735 |
371(c)(1),(2),(4) Date: |
June 07, 2016 |
PCT
Pub. No.: |
WO2015/086327 |
PCT
Pub. Date: |
June 18, 2015 |
Prior Publication Data
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|
|
|
Document
Identifier |
Publication Date |
|
US 20160313416 A1 |
Oct 27, 2016 |
|
Foreign Application Priority Data
|
|
|
|
|
Dec 10, 2013 [EP] |
|
|
13196463 |
|
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G01R
33/5611 (20130101); G01R 33/5608 (20130101); G01R
33/246 (20130101) |
Current International
Class: |
G01R
33/24 (20060101); G01R 33/56 (20060101); G01R
33/561 (20060101) |
Field of
Search: |
;324/307,311,300,200,301,309 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
Other References
Murakami et al "Intensity Correction of Phased-Array Surface Coil
Images" Magnetic Resonance in Medicine vol. 35, No. 4, Apr. 1, 1996
p. 585-590. cited by applicant .
Bydder et al "Generalized Smash Imaging" Magnetic Resonance in
Medicine vol. 47, p. 160-170 (2002). cited by applicant .
Liu et al "Auto-Calibrated Parallel Imaging Reconstruction for
Arbitrary Trajectories Using K-Space Sparse Matrices" IEEE
Transactions on Medical Imaging vol. 29, No. 3, Mar. 2010 p.
950-959. cited by applicant .
Cheng et al "Magnetic Resonance Imaging Image Intensity Correction
With Extrapolation and Advanced Smoothing" Magnetic Resonance in
Medicine, vol. 55, p. 959-966 (2006). cited by applicant .
Gabr et al "Deconvolution-Interpolation Gridding(Ding): Accurate
Reconstruction for Arbitrary K-Space Trajectories" Magnetic
Resonance in Medicine, vol. 56, p. 1182-1191 (2006). cited by
applicant .
Uecker et al "Espirit--An Eigenvalue Approach to Autocalibrating
Parallel MRI . . . " Magnetic Reson. Med. vol. 71(3) p. 990-1001.
cited by applicant .
V. Rasche et al "Resampling of Data Between Arbitrary Grids Using
Convolution Interpolation" IEEE Transactions on Medical Imaging,
vol. 18, No. 5, May 1999 pp. 385-392. cited by applicant.
|
Primary Examiner: Astacio-Oquendo; Giovanni
Claims
The invention claimed is:
1. A magnetic resonance imaging system, comprising: a radio
frequency system for acquiring magnetic resonance data of a subject
within an imaging zone, wherein the radio frequency system
comprises a coil with multiple antenna elements operable for
acquiring the magnetic resonance data; a non-transitory computer
readable memory for storing machine readable instructions; a
processor for controlling the magnetic resonance imaging system,
wherein execution of the machine readable instructions cause the
processor to: acquire calibration magnetic resonance data from a
first field of view within the imaging zone using each of the
multiple antenna elements; calculate modified magnetic resonance
data by interpolating the calibration magnetic resonance data to a
second field of view within the imaging zone, wherein the second
field of view encompasses and is larger than the first field of
view; calculate a coil sensitivity kernel for each of the multiple
antenna elements by deconvolving the modified magnetic resonance
data for each of the multiple antenna elements; and calculate a
coil sensitivity for each of the multiple antenna elements by
transforming each coil sensitivity kernel into image space.
2. The magnetic resonance imaging system of claim 1, wherein the
modified magnetic resonance data is calculated by: reconstructing a
first magnetic resonance image for each antenna element using the
calibration magnetic resonance data; calculating a modified
magnetic resonance image for each antenna element, wherein each
modified magnetic resonance image is defined by the second field of
view and is calculated by pasting the first magnetic resonance
image into a null valued image; and calculating the modified
magnetic resonance data by Fourier transforming the modified
magnetic resonance image.
3. The magnetic resonance imaging system of claim 1, wherein the
modified magnetic resonance data is calculated by interpolating the
calibration magnetic resonance data for each of the multiple
antenna elements to a predefined set of points in Fourier space,
wherein the predefined set of points in Fourier space represent the
second field of view.
4. The magnetic resonance imaging system of claim 3, wherein the
modified magnetic resonance data for each of the antenna elements
comprises a first set of points in Fourier space, and wherein the
predefined set of points in Fourier space comprises the first set
of points in Fourier space.
5. The magnetic resonance imaging system of claim 4, wherein
execution of the instructions further cause the processor to
generate the predetermined set of points in Fourier space by
translating a unit cell.
6. The magnetic resonance imaging system of claim 1, wherein the
radio frequency system further comprises a body coil; wherein
execution of the instructions further causes the processor to:
acquire body coil magnetic resonance data from the first field of
view using the body coil during acquisition of the calibration
magnetic resonance data, and calculate modified body coil magnetic
resonance data by interpolating the body coil magnetic resonance
data to the second field of view; and wherein the coil sensitivity
kernel for each of the multiple antenna elements is deconvolved
with respect to the modified body coil magnetic resonance data.
7. The magnetic resonance imaging system of claim 1, wherein
execution of the instructions cause the processor to deconvolve the
coil sensitivity kernel for each of the multiple antenna elements
by initially setting a reference image to a predetermined value;
wherein execution of the instructions cause the processor to
further deconvolve the coil sensitivity kernel for each of the
multiple antenna elements by iteratively repeating the following
steps: calculate an intermediate coil sensitivity kernel by
deconvolving the modified magnetic resonance data for each of the
multiple antenna elements with respect to a Fourier transform of
the reference image, calculate an intermediate coil sensitivity for
each of the multiple antenna elements by transforming each
intermediate coil sensitivity kernel into image space, and
recalculate the reference image using the intermediate coil
sensitivities and the calibration magnetic resonance data; and
wherein the iterative steps are repeated a predetermined number of
time or when the reference image has converged within a
predetermined statistical measure.
8. The magnetic resonance imaging system of claim 7, wherein the
predetermined value of the reference image is a uniform value.
9. The magnetic resonance imaging system of claim 1, wherein the
memory further contains pulse sequence data descriptive of a
parallel imaging magnetic resonance technique, wherein execution of
the instructions further cause the processor to: acquire imaging
magnetic resonance data using the pulse sequence data to control
the magnetic resonance imaging system from the first field of view;
and reconstruct a magnetic resonance image using the imaging
magnetic resonance data and the corrected coil sensitivity for each
of the multiple antenna elements.
10. The magnetic resonance imaging system of claim 1, wherein
execution of the instructions further cause the processor to
recalculate the coil sensitivity for each of the multiple antenna
elements by reducing the coil sensitivity to the first field of
view.
11. A method of operating a magnetic resonance imaging system,
wherein the magnetic resonance imaging system comprises a radio
frequency for acquiring magnetic resonance data of a subject from
an imaging zone, wherein the radio frequency system comprises a
coil with multiple antenna elements operable for acquiring the
magnetic resonance data, wherein the method comprises the steps of:
acquiring calibration magnetic resonance data from a first field of
view within the imaging zone using each of the multiple antenna
elements; calculating modified magnetic resonance data by
interpolating the calibration magnetic resonance data to a second
field of view within the imaging zone, wherein the second field of
view encompasses and is larger than the first field of view;
calculating a coil sensitivity kernel by deconvolving the modified
magnetic resonance data for each of the multiple antenna elements;
calculating a coil sensitivity for each of the multiple antenna
elements by transforming each coil sensitivity kernel into image
space; acquiring imaging magnetic resonance data from the first
field of view using a parallel imaging technique; reconstructing
the acquired imaging magnetic resonance data into a magnetic
resonance image using the calculated coil sensitivity for each of
the multiple antenna elements; and controlling a display device to
display the reconstructed magnetic resonance image.
12. The method of claim 11, wherein the modified magnetic resonance
data is calculated by: reconstructing a first calibration magnetic
resonance image for each antenna element using the calibration
magnetic resonance data; calculating a modified calibration
magnetic resonance image for each antenna element, wherein each
modified calibration magnetic resonance image is defined by the
second field of view and is calculated by pasting the first
calibration magnetic resonance image into a null valued image; and
calculating the modified magnetic resonance data by Fourier
transforming the modified calibration magnetic resonance image.
13. A non-transitory computer-readable medium carrying machine
readable instructions for a processor for controlling a magnetic
resonance imaging system, wherein the magnetic resonance imaging
system includes a radio frequency system for acquiring magnetic
resonance data of a subject from an imaging zone, wherein the radio
frequency system comprises a coil with multiple antenna elements
operable for acquiring the magnetic resonance data, wherein
execution of the instructions causes the processor to: acquire
calibration magnetic resonance data from a first field of view
within the imaging zone using each of the multiple antenna
elements; calculate modified magnetic resonance data by
interpolating the calibration magnetic resonance data to a second
field of view within the imaging zone, wherein the second field of
view encompasses and is larger than the first field of view;
calculate a coil sensitivity kernel by deconvolving the modified
magnetic resonance data for each of the multiple antenna elements;
and calculate a coil sensitivity for each of the multiple antenna
elements by transforming each coil sensitivity matrix kernel into
image space.
14. The non-transitory computer-readable medium of claim 13,
wherein the modified magnetic resonance data is calculated by:
reconstructing a first magnetic resonance image for each antenna
element using the calibration magnetic resonance data; calculating
a modified magnetic resonance image for each antenna element,
wherein each modified magnetic resonance image is defined by the
second field of view and is calculated by pasting the first
magnetic resonance image into a null valued image; and calculating
the modified magnetic resonance data by Fourier transforming the
modified magnetic resonance image.
15. The non-transitory computer-readable medium of claim 13,
wherein the modified magnetic resonance data is calculated by
interpolating the calibration magnetic resonance data for each of
the multiple antenna elements to a predefined set of points in
Fourier space, wherein the predefined set of points in Fourier
space represent the second field of view.
16. The non-transitory computer-readable medium of claim 13,
wherein the instructions further control the processor to: acquire
imaging magnetic resonance data from the first field of view using
a parallel imaging technique; reconstruct the acquired imaging
magnetic resonance data into a magnetic resonance image using the
calculated coil sensitivity for each of the multiple antenna
elements; and control a display device to display the reconstructed
magnetic resonance image.
17. A magnetic resonance imaging system comprising: a radio
frequency coil with multiple antenna elements, each antenna element
being configured to acquire magnetic resonance data from a subject
within an imaging zone; one or more processors configured to:
receive calibration magnetic resonance data from a first field of
view within the imaging zone using each of the multiple antenna
elements, calculate modified calibration magnetic resonance data by
interpolating the calibration magnetic resonance data to a second
field of view within the imaging zone, wherein the second field of
view encompasses and is larger than the first field of view,
calculate a coil sensitivity kernel for each of the multiple
antenna elements by deconvolving the modified magnetic resonance
data, calculate a coil sensitivity for each of the multiple antenna
elements by transforming each coil sensitivity kernel into image
space, receive imaging magnetic resonance data generated using a
parallel imaging technique from the first field of view,
reconstruct the acquired imaging magnetic resonance data into a
magnetic resonance image using the calculated coil sensitivity for
each of the multiple antenna elements, and control a display device
to display the reconstructed magnetic resonance image.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
This application is a U.S. national phase application of
International Application No. PCT/EP2014/075735, filed on Nov. 27,
2014, which claims the benefit of EP Application Serial No.
13196463.7 filed on Dec. 10, 2013 and is incorporated herein by
reference.
TECHNICAL FIELD
The invention relates to parallel magnetic resonance imaging using
multiple antenna elements, in particular it relates to methods and
apparatuses for calculating the coil sensitivities for the multiple
antenna elements.
BACKGROUND OF THE INVENTION
In parallel magnetic resonance imaging, a portion of the k-space
being measured is acquired by multiple antenna elements
simultaneously. The data acquired by the multiple antenna elements
is combined to make a complete magnetic resonance image. Typically
sensitive surface coil elements are placed around a subject. The
image data collected by each of the multiple antenna elements is
combined together using spatially dependent coil sensitivity for
each coil element.
Typically the coil sensitivities are determined during a
calibration step. One way of calibrating the multiple elements is
to acquire data at the same time using a Quadrature Body Coil (QBC)
as well as the multiple antenna elements. A QBC is not particularly
sensitive; however they acquire data with a good spatial uniformity
which can be used to construct a reference image. The coil
sensitivity for a particular coil element can be calculated by
dividing the image from the coil element by the reference
image.
In the journal article Uccker et. al., "ESPIRiT--An Eigenvalue
Approach to Autocalibrating Parallel MRI: Where SENSE Meets
GRAPPA," Magnetic Resonance in Medicine, online, May 6, 2013
discloses a method of calculating coil sensitivities by a k-space
based method to calculate coil sensitivities as Eigen vectors of a
linear system.
U.S. Pat. No. 6,380,741 B1 discloses using a pair of r.f. receive
coils together with sensitive information concerning those coils to
unfurl the aliased images to produce a full image.
SUMMARY OF THE INVENTION
The invention provides for a magnetic resonance imaging system, a
method of operating the magnetic resonance imaging system, and a
computer program product in the independent claims. Embodiments are
given in the dependent claims.
As will be appreciated by one skilled in the art, aspects of the
present invention may be embodied as an apparatus, method or
computer program product. Accordingly, aspects of the present
invention may take the form of an entirely hardware embodiment, an
entirely software embodiment (including firmware, resident
software, micro-code, etc.) or an embodiment combining software and
hardware aspects that may all generally be referred to herein as a
"circuit," "module" or "system." Furthermore, aspects of the
present invention may take the form of a computer program product
embodied in one or more computer readable medium(s) having computer
executable code embodied thereon.
Any combination of one or more computer readable medium(s) may be
utilized. The computer readable medium may be a computer readable
signal medium or a computer readable storage medium. A
`computer-readable storage medium` as used herein encompasses any
tangible storage medium which may store instructions which are
executable by a processor of a computing device. The
computer-readable storage medium may be referred to as a
computer-readable non-transitory storage medium. The
computer-readable storage medium may also be referred to as a
tangible computer readable medium. In some embodiments, a
computer-readable storage medium may also be able to store data
which is able to be accessed by the processor of the computing
device. Examples of computer-readable storage media include, but
are not limited to: a floppy disk, a magnetic hard disk drive, a
solid state hard disk, flash memory, a USB thumb drive, Random
Access Memory (RAM), Read Only Memory (ROM), an optical disk, a
magneto-optical disk, and the register file of the processor.
Examples of optical disks include Compact Disks (CD) and Digital
Versatile Disks (DVD), for example CD-ROM, CD-RW, CD-R, DVD-ROM,
DVD-RW, or DVD-R disks. The term computer readable-storage medium
also refers to various types of recording media capable of being
accessed by the computer device via a network or communication
link. For example a data may be retrieved over a modem, over the
internet, or over a local area network. Computer executable code
embodied on a computer readable medium may be transmitted using any
appropriate medium, including but not limited to wireless, wire
line, optical fiber cable, RF, etc., or any suitable combination of
the foregoing.
A computer readable signal medium may include a propagated data
signal with computer executable code embodied therein, for example,
in baseband or as part of a carrier wave. Such a propagated signal
may take any of a variety of forms, including, but not limited to,
electro-magnetic, optical, or any suitable combination thereof. A
computer readable signal medium may be any computer readable medium
that is not a computer readable storage medium and that can
communicate, propagate, or transport a program for use by or in
connection with an instruction execution system, apparatus, or
device.
`Computer memory` or `memory` is an example of a computer-readable
storage medium. Computer memory is any memory which is directly
accessible to a processor. `Computer storage` or `storage` is a
further example of a computer-readable storage medium. Computer
storage is any non-volatile computer-readable storage medium. In
some embodiments computer storage may also be computer memory or
vice versa.
A `processor` as used herein encompasses an electronic component
which is able to execute a program or machine executable
instruction or computer executable code. References to the
computing device comprising "a processor" should be interpreted as
possibly containing more than one processor or processing core. The
processor may for instance be a multi-core processor. A processor
may also refer to a collection of processors within a single
computer system or distributed amongst multiple computer systems.
The term computing device should also be interpreted to possibly
refer to a collection or network of computing devices each
comprising a processor or processors. The computer executable code
may be executed by multiple processors that may be within the same
computing device or which may even be distributed across multiple
computing devices.
Computer executable code may comprise machine executable
instructions or a program which causes a processor to perform an
aspect of the present invention. Computer executable code for
carrying out operations for aspects of the present invention may be
written in any combination of one or more programming languages,
including an object oriented programming language such as Java,
Smalltalk, C++ or the like and conventional procedural programming
languages, such as the "C" programming language or similar
programming languages and compiled into machine executable
instructions. In some instances the computer executable code may be
in the form of a high level language or in a pre-compiled form and
be used in conjunction with an interpreter which generates the
machine executable instructions on the fly.
The computer executable code may execute entirely on the user's
computer, partly on the user's computer, as a stand-alone software
package, partly on the user's computer and partly on a remote
computer or entirely on the remote computer or server. In the
latter scenario, the remote computer may be connected to the user's
computer through any type of network, including a local area
network (LAN) or a wide area network (WAN), or the connection may
be made to an external computer (for example, through the Internet
using an Internet Service Provider).
Aspects of the present invention are described with reference to
flowchart illustrations and/or block diagrams of methods, apparatus
(systems) and computer program products according to embodiments of
the invention. It will be understood that each block or a portion
of the blocks of the flowchart, illustrations, and/or block
diagrams, can be implemented by computer program instructions in
form of computer executable code when applicable. It is further
understood that, when not mutually exclusive, combinations of
blocks in different flowcharts, illustrations, and/or block
diagrams may be combined. These computer program instructions may
be provided to a processor of a general purpose computer, special
purpose computer, or other programmable data processing apparatus
to produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or
blocks.
These computer program instructions may also be stored in a
computer readable medium that can direct a computer, other
programmable data processing apparatus, or other devices to
function in a particular manner, such that the instructions stored
in the computer readable medium produce an article of manufacture
including instructions which implement the function/act specified
in the flowchart and/or block diagram block or blocks.
The computer program instructions may also be loaded onto a
computer, other programmable data processing apparatus, or other
devices to cause a series of operational steps to be performed on
the computer, other programmable apparatus or other devices to
produce a computer implemented process such that the instructions
which execute on the computer or other programmable apparatus
provide processes for implementing the functions/acts specified in
the flowchart and/or block diagram block or blocks.
A `user interface` as used herein is an interface which allows a
user or operator to interact with a computer or computer system. A
`user interface` may also be referred to as a `human interface
device.` A user interface may provide information or data to the
operator and/or receive information or data from the operator. A
user interface may enable input from an operator to be received by
the computer and may provide output to the user from the computer.
In other words, the user interface may allow an operator to control
or manipulate a computer and the interface may allow the computer
indicate the effects of the operator's control or manipulation. The
display of data or information on a display or a graphical user
interface is an example of providing information to an operator.
The receiving of data through a keyboard, mouse, trackball,
touchpad, pointing stick, graphics tablet, joystick, gamepad,
webcam, headset, gear sticks, steering wheel, pedals, wired glove,
dance pad, remote control, and accelerometer are all examples of
user interface components which enable the receiving of information
or data from an operator.
A `hardware interface` as used herein encompasses an interface
which enables the processor of a computer system to interact with
and/or control an external computing device and/or apparatus. A
hardware interface may allow a processor to send control signals or
instructions to an external computing device and/or apparatus. A
hardware interface may also enable a processor to exchange data
with an external computing device and/or apparatus. Examples of a
hardware interface include, but are not limited to: a universal
serial bus, IEEE 1394 port, parallel port, IEEE 1284 port, serial
port, RS-232 port, IEEE-488 port, Bluetooth connection, Wireless
local area network connection, TCP/IP connection, Ethernet
connection, control voltage interface, MIDI interface, analog input
interface, and digital input interface.
A `display` or `display device` as used herein encompasses an
output device or a user interface adapted for displaying images or
data. A display may output visual, audio, and or tactile data.
Examples of a display include, but are not limited to: a computer
monitor, a television screen, a touch screen, tactile electronic
display, Braille screen, Cathode ray tube (CRT), Storage tube,
Bistable display, Electronic paper, Vector display, Flat panel
display, Vacuum fluorescent display (VF), Light-emitting diode
(LED) displays, Electroluminescent display (ELD), Plasma display
panels (PDP), Liquid crystal display (LCD), Organic light-emitting
diode displays (OLED), a projector, and Head-mounted display.
Magnetic Resonance (MR) data is defined herein as being the
recorded measurements of radio frequency signals emitted by atomic
spins by the antenna of a Magnetic resonance apparatus during a
magnetic resonance imaging scan. Magnetic resonance data is an
example of medical image data. A Magnetic Resonance Imaging (MRI)
image is defined herein as being the reconstructed two or three
dimensional visualization of anatomic data contained within the
magnetic resonance imaging data. This visualization can be
performed using a computer.
Magnetic resonance location data as used herein encompasses
magnetic resonance data that is acquired for determining the
location of a fiducial marker.
In one aspect the invention provides for a magnetic resonance
imaging system. The magnetic resonance imaging system comprises a
radio-frequency system for acquiring magnetic resonance data of a
subject from an imaging zone. The radio-frequency system comprises
a coil with multiple antenna elements operable for acquiring the
magnetic resonance data. In magnetic resonance imaging technology
the antennas used for sending and receiving radio-frequency signals
are typically referred to as coils. The multiple antenna elements
may be operable for receiving separate radio-frequency
transmissions from the subject. The magnetic resonance imaging
system further comprises a memory for storing machine-readable
instructions. The magnetic resonance imaging system further
comprises a processor for controlling the magnetic resonance
imaging system.
Execution of the instructions causes the processor to acquire
calibration magnetic resonance data from a first field of view
within the imaging zone using each of the multiple antenna
elements. The calibration magnetic resonance data is data which is
used later to calculate the coil sensitivities for each of the
multiple antenna elements. Execution of the instructions further
cause the processor to calculate modified magnetic resonance data
by interpolating the calibration magnetic resonance data to a
second field of view within the imaging zone. The second field of
view encompasses and is larger than the first field of view. The
fields of view may be volumes. In this case the first field of view
is a volume within the second field of view. The interpolation of
the calibration magnetic resonance data may be accomplished several
different ways. It may for instance be done in image space or it
may also be done by performing explicit interpolations within
k-space.
Execution of the instructions further cause the processor to
calculate a coil sensitivity kernel by deconvolving the modified
magnetic resonance data for each of the magnetic resonance antenna
elements. The signal received or measured at a particular point in
k-space by a particular coil I can be considered to be the Fourier
transform of the coil sensitivity x the magnetization of the
subject in the region being examined. This can be expressed as:
s.sub.i=(c.sub.im), where s.sub.i represents the measurements in
k-space for coil i, is the Fourier transform, c.sub.i is the coil
spatially dependent coil sensitivity for coil i, and m is the
spatially dependent magnetization in Field of View (FOV).
This equation can be re-written as: s.sub.i=c.sub.i*m.
The quantity c.sub.i represents the coil sensitivity kernel, which
can be calculated via deconvolution. This is because s.sub.i is
measured and m can be measured or calculated in an iterative
process. Taking the Fourier transform of c.sub.i yields the coil
sensitivity c.sub.i.
In practical terms c.sub.i can be chosen to be a kernel of a
particular size. For instance a 16.times.16 block of values (or
other block of values) can be chosen and calculated using
deconvolution. Once the coil sensitivity kernels are known then the
coil sensitivity kernel can be Fourier transformed back into image
space and used as a coil sensitivity directly.
In parallel imaging techniques a large number or multiple antenna
elements are used to record the same magnetic resonance data at the
same time. Each antenna element receives slightly different
information and there needs to be a calibration in order to
reconstruct the image from all the different antenna elements. This
can be accomplished purely through image-based techniques. However,
there are disadvantages in using image processing. For instance if
there is an air bubble or other region of the subject which does
not produce a magnetic resonance or NMR signal then the calibration
for this particular region may not be done. This may lead to
problems when using the calibrated coil sensitivities to form a
parallel imaging technique. This may lead to artifacts or ghosts in
the end image. The magnetic resonance imaging system as described
above may result in a magnetic resonance imaging system which has
greatly reduced artifacts when performing parallel imaging.
Examples may relate to a magnetic resonance imaging method which
involves parallel imaging. To unfold aliasing due to under sampling
in k-space of the magnetic resonance signals, the coil sensitivity
profiles of the RF receiver coils are required. The coil
sensitivity profiles are represented by k-space kernels. Normally
relatively small kernels (i.e. having a local support in k-space)
are employed by virtue of the smooth spatial variations of the coil
sensitivity profiles.
The k-space kernel for an individual RF coil may be calculated by
deconvolution of the magnetic resonance signals acquired by the
loaded (by the object to be imaged) RF coil and k-space reference
data. Before the deconvolution is carried out, the field of view is
extended to include empty space around the object.
This embodiment may enable better account for the monotonous decay
of the coil sensitivity profile from the outline of the object.
Notably errors due to not properly taking into account of the
object's edge and which could lead to residual unfolding artifacts
are avoided. Often extending the field of view by making it twice
as large as the existing field of view provides good results.
Accurate coil sensitivity maps (CSMs) are used for parallel MR
imaging methods. Embodiments may provide a method to calculate CSMs
by a deconvolution in k-space. The proposed method may have the
following benefits over known methods:
Requires less data than image based CSM calculation. I.e., it
enables fast acquisition of CSM data.
More accurate CSM near edges of the object resulting in less
fold-over artifacts. This is also relevant in situations where
there are signal voids within the imaged object.
Less susceptible to noisy data.
Orders of magnitude faster than other k-space based methods (e.g.
ESPIRiT).
In another embodiment the modified magnetic resonance data is
calculated by reconstructing a first magnetic resonance image for
each antenna element using the calibration magnetic resonance data.
The modified magnetic resonance data is further calculated by
calculating a modified magnetic resonance image for each antenna
element. Each modified magnetic resonance image is defined by the
second field of view and is calculated by pasting the first
magnetic resonance image into a null valued image. The first field
of view is within the second field of view. Essentially the data
from the first magnetic resonance image is copied into the modified
magnetic resonance image. Any data in the modified magnetic
resonance image that did not come from the first magnetic resonance
image is set to null or zero. This may also be described as
expanding the field of view and then padding the expanded area with
zeros. The modified magnetic resonance data is further calculated
by calculating the modified magnetic resonance data by Fourier
transforming the modified magnetic resonance image.
This embodiment may be beneficial because it provides an efficient
and easy way of interpolating the calibration magnetic resonance
data to a second field of view. The coil sensitivity kernels and
coil sensitivities calculated using this embodiment result in fewer
artifacts.
In another embodiment the modified magnetic resonance data is
calculated by interpolating the calibration of the magnetic
resonance data for each of the multiple antenna elements to a
predefined set of points in Fourier space. The predefined set of
points in Fourier space represent the second field of view. The
interpolation of points in Fourier space for magnetic resonance
imaging is known from the Paper by Rische et. al., "Resampling of
Data Between Arbitrary Grids Using Convolution Interpolation, IEEE
Trans. on Medical Imaging, Vol. 198, 1999, pp. 385-392. This
embodiment also may provide an efficient means of using the effects
in magnetic resonance imaging using parallel imaging
techniques.
In another embodiment the modified magnetic resonance data for each
of the antenna elements comprises a first set of points in Fourier
space. The predefined set of points in Fourier space comprises the
first set of points in Fourier space. This embodiment may be
beneficial because when the predefined set of points are chosen or
decided upon the measured points are reused.
In another embodiment execution of the instructions further cause
the processor to generate the predetermined set of points in
Fourier space by translating a unit cell. A unit cell as used
herein encompasses a set of points in Fourier space which may be
translated to different positions. In some embodiments the unit
cell is the same size as the coil sensitivity kernel. This
embodiment may have the advantage that the coil sensitivity kernel
may be efficiently calculated.
The grid used to interpolate to is used to calculate the kernel. To
be suitable for this task there are two requirements which are
beneficial to be fulfilled:
1. The grid must be generated by translating a unit cell (i.e. it
must be like a crystal lattice). Cartesian or hexagonal is ok but
radial and spiral is not. This requirement is a must because if it
is not fulfilled the convolution cannot be written as a linear
system of equations using a discrete kernel. 2. The grid must
encode a field of view which has sufficient empty space around the
object. This requirement is less strict than the one above because
you can argue about how much space is sufficient. This depends on
the coil geometry and on the targeted level of accuracy of the
CSM.
The background of this is that the CSM defined by a k-space kernel
is always periodic with the field of view. I.e. the value of the
CSM at the left and right edge of the image is the same.
But on the other hand, the sensitivity of typical surface coils has
a strong dependency on distance. I.e. the CSM has a high value on
one edge of the object and a low value on the opposite edge.
This second requirement ensures that the edges of the object are
not too close to the edges of the image because then the true CSM
cannot be modeled by a kernel.
It should be possible to estimate how much empty space around the
object is required based on the chosen kernel width and dynamic
range of the true CSM.
Any grid that fulfills both requirements can be used. It is
expected that a grid which shares points with the grid used for
data acquisition is beneficial because then the acquired data can
be used at the common points without the need for interpolation. If
the data is acquired on the correct grid in the first place, you do
not need interpolation at all. But this may not be preferred
because the acquisition is time consuming and does not add new
information. Interpolation in k-space is a more efficient way to
generate the values on the grid for a larger field of view (and
zero-padding in image space is just a particularly efficient method
to interpolate from a given Cartesian grid to a Cartesian grid with
finer spacing).
In another embodiment the radio-frequency system further comprises
a body coil. Execution of the instructions further cause the
processor to acquire body coil magnetic resonance data from the
first field of view using the body coil during acquisition of the
calibration magnetic resonance data. Execution of the instructions
further cause the processor to calculate modified body coil
magnetic resonance data by interpolating the body coil magnetic
resonance data to the second field of view. For instance this
interpolation may be performed using the image-based method that
was described above where the body coil magnetic resonance data is
reconstructed into an image and then placed into the second field
of view and then retransformed back into Fourier space.
Alternatively the body coil magnetic resonance data can be
interpolated to the predefined set of points in Fourier space. The
coil sensitivity kernel for each of the multiple antenna elements
is deconvolved with respect to the modified body coil magnetic
resonance data. In this embodiment a body coil which while not
overly sensitive is useful for acquiring a uniform measurement of
the subject. The body coil or quadrature body coil is used to
perform and acquire reference data which is then used in the
deconvolution process.
In another embodiment execution of the instructions cause the
processor to deconvolve the coil sensitivity kernel for each of the
multiple antenna elements by initially setting a reference image to
a predetermined value. Execution of the instructions cause the
processor to further deconvolve the coil sensitivity kernel for
each of the multiple antenna elements by iteratively repeating the
following steps: the first iterative step is to calculate an
intermediate coil sensitivity kernel by deconvolving the modified
magnetic resonance data for each of the multiple antenna elements
with respect to a Fourier transform of the reference image. The
second iterative step is then to calculate an intermediate coil
sensitivity for each of the multiple antenna elements by
transforming each intermediate coil sensitivity kernel into image
space. The third iterative step is to recalculate the reference
image using the intermediate coil sensitivities and the calibration
magnetic resonance data.
These iterative steps are then repeated a predetermined number of
times or until the reference image has converged to within a
predetermined statistical measure. For instance if the average
change between the reference image and the previously used
reference image is less than a predetermined amount then the method
may stop. In this method instead of using the measurement from a
body coil an image set to a predetermined value is simply used
instead. The deconvolution process is performed and then the
results are used with the resultant coil sensitivities with the
calibration magnetic resonance data to reconstruct a new reference
image. The process is then repeated over and over again for a fixed
number of cycles or until the image has converged. This embodiment
may be beneficial because it eliminates the need of using a body
coil. It is also superior to using measurement from a single one of
the multiple antenna elements because the multiple antenna elements
may not acquire data uniformly from the whole subject.
The iterative method can also be described as follows. The signal
s.sub.i of the surface antenna elements is described by the
following model: s.sub.i=, where the hat denotes the
Fourier-transform, m is the magnetization of the object, and
c.sub.i is the coil sensitivity. I.e. each surface antenna element
senses the same object magnetization weighted according to its own
sensitivity profile.
A common assumption is that the QBC has uniform receive sensitivity
(QBC=1). In this case, the QBC signal can be used as reference for
the coil sensitivities and the above equation can be transformed
into: s.sub.i==*{circumflex over (m)}=*s.sub.QBC.
The value of c.sub.i can be calculated from this equation by e.g.
deconvolution if the surface coil signal and the QBC signals are
acquired. If the QBC signal is not acquired, a different reference
image must be used. In principle the reference image can be chosen
arbitrarily. The resulting coil sensitivities will be relative to
the chosen reference image. A common choice for the reference image
is to use the sum-of-squares (SoS) image
.SIGMA..sub.is.sub.i*s.sub.i, where the star denotes the complex
conjugate.
However, this choice of reference image is not optimal if the coil
sensitivities are to be represented by small kernels in k-space.
This is because the phase of the SoS image is always zero. As a
consequence, the phase of the estimated coil sensitivity is
directly given by the phase of the surface coil image which may not
be smooth enough to be represented by a small kernel. I.e. with
this choice of reference image the coil sensitivities resulting
from the deconvolution method may have a high error if the kernel
size is chosen too small.
Instead of using the SoS image it is better to chose a reference
image that has a non-zero phase. One way to a good reference image
is to compute it in an iterative procedure. Initialization: Start
with a homogenous reference image: m.sub.0=1 Iteration: 1.
Calculate c.sub.i,k by deconvolution from
s.sub.i=c.sub.i,k*{circumflex over (m)}.sub.k.
2. Set
m.sub.k+1=.SIGMA..sub.ic.sub.i,k*s.sub.i/.SIGMA..sub.ic.sub.i,k*c.-
sub.i,k
This iteration converges very quickly to a stable reference image.
Typically it can be terminated after 3 iterations. I.e. m.sub.3
should be used as reference image instead of the SoS image.
In another embodiment the predetermined value of the reference
image is a uniform value. This embodiment may be beneficial because
it may not be necessary to know or have an accurate guess of what
the subject looks like before starting the iterative method.
In another embodiment the memory further contains pulse sequence
data descriptive of a parallel imaging magnetic resonance imaging
technique. The pulse sequence data enables the processor to acquire
magnetic resonance data using the parallel imaging magnetic
resonance technique. In parallel imaging a reduced amount of
k-space data is acquired from an array or multiple antenna
elements. Some well-known techniques are for example the SENSE
technique. Another well-known parallel imaging technique is the
so-called GRAPPA technique.
Execution of the instructions further causes the processor to
acquire imaging magnetic resonance data using the pulse sequence
data to control the magnetic resonance imaging system. In this case
the multiple antenna elements are used to acquire the magnetic
resonance data. Each of the multiple antenna elements acquires a
limited area of the k-space data. Next, execution of the
instructions further cause the processor to reconstruct a magnetic
resonance image using the imaging magnetic resonance data and the
coil sensitivities for each of the multiple antenna elements. This
embodiment may be beneficial because it may enable reconstructing
the magnetic resonance imaging with fewer artifacts.
In another embodiment execution of the instructions further cause
the processor to recalculate the coil sensitivities for each of the
multiple antenna elements by cropping its coil sensitivity to the
first field of view. This may be beneficial in saving memory space.
When performing parallel imaging, coil sensitivities outside of the
first field of view will not be used.
In another aspect the invention provides for a method of operating
the magnetic resonance imaging system. The magnetic resonance
imaging system comprises a radio-frequency system for acquiring
magnetic resonance data of a subject from an imaging zone. The
radio-frequency system comprises a coil with multiple antenna
elements operable for acquiring the magnetic resonance data. The
method comprises the step of acquiring calibration magnetic
resonance data from a first field of view within the imaging zone
using each of the multiple antenna elements. The method further
comprises the step of calculating modified magnetic resonance data
by interpolating the calibration magnetic resonance data to a
second field of view within the imaging zone. The second field of
view encompasses and is larger than the first field of view. The
method further comprises the step of calculating a coil sensitivity
kernel by deconvolving the modified magnetic resonance data for
each of the multiple antenna elements. The method further comprises
the step of calculating a coil sensitivity for each of the multiple
antenna elements by transforming each coil sensitivity matrix
kernel into image space.
In another embodiment the modified magnetic resonance data is
calculated by reconstructing a first magnetic resonance image for
each antenna element using the calibration magnetic resonance data.
The modified magnetic resonance data is further calculated by
calculating a modified magnetic resonance image for each antenna
element. Each modified magnetic resonance image is defined by the
second field of view and is calculated by pasting the first
magnetic resonance image into a null valued image. The modified
magnetic resonance data is further calculated by calculating the
modified magnetic resonance data by Fourier transforming the
modified magnetic resonance image.
In another aspect the invention provides for a computer program
product comprising machine-readable instructions for a processor
for controlling a magnetic resonance imaging system. The magnetic
resonance imaging system comprises a radio-frequency system for
acquiring magnetic resonance data of a subject from an imaging
zone. The radio-frequency system comprises a coil with multiple
antenna elements operable for acquiring the magnetic resonance
data. Execution of the instructions causes the processor to acquire
calibration magnetic resonance data from a first field of view
within the imaging zone using each of the multiple antenna
elements. Execution of the instructions further cause the processor
to calculate modified magnetic resonance data by interpolating the
calibration magnetic resonance data to a second field of view
within the imaging zone. The second field of view encompasses and
is larger than the first field of view. Execution of the
instructions further causes the processor to calculate a coil
sensitivity kernel by deconvolving the modified magnetic resonance
data for each of the multiple antenna elements. Execution of the
instructions further cause the processor to calculate a coil
sensitivity for each of the multiple antenna elements by
transforming each coil sensitivity matrix kernel into image
space.
In another embodiment the modified magnetic resonance data is
calculated by reconstructing a first magnetic resonance image for
each antenna element using the calibration magnetic resonance data.
The modified magnetic resonance data is further calculated by
calculating a modified magnetic resonance image for each antenna
element. Each modified magnetic resonance image is defined by the
second field of view and is calculated by pasting the first
magnetic resonance image into a null valued image. The modified
magnetic resonance data is further calculated by calculating the
modified magnetic resonance data by Fourier transforming the
modified magnetic resonance image.
In another embodiment the modified magnetic resonance data is
calculated by interpolating the calibration magnetic resonance data
for each of the multiple antenna elements to a predefined set of
points in Fourier space. The predefined set of points in Fourier
space represents the second field of view.
It is understood that one or more of the aforementioned embodiments
of the invention may be combined as long as the combined
embodiments are not mutually exclusive.
BRIEF DESCRIPTION OF THE DRAWINGS
In the following preferred embodiments of the invention will be
described, by way of example only, and with reference to the
drawings in which:
FIG. 1 illustrates an example of a magnetic resonance imaging
system;
FIG. 2 illustrates an example of a method of operating the magnetic
resonance imaging system of FIG. 1;
FIG. 3 further illustrates a variant of the method illustrated in
FIG. 2;
FIG. 4 further illustrates a variant of the method illustrated in
FIG. 2;
FIG. 5 further illustrates a variant of the method illustrated in
FIG. 2;
FIG. 6 further illustrates a variant of the method illustrated in
FIG. 2;
FIG. 7 shows a plot of the magnetization for a one-dimensional
object;
FIG. 8 shows a plot of the calculated CSM using several different
methods for the data in FIG. 7;
FIG. 9 shows a plot comparing the calculated CSMs of FIG. 8;
FIG. 10 shows a SENSE magnetic resonance image calculated using
CSMs calculated using the traditional image division method;
FIG. 11 shows the SENSE magnetic resonance image of FIG. 10
calculated using CSMs calculated using deconvolution;
FIG. 12 shows the SENSE magnetic resonance image of FIG. 10
calculated using CSMs calculated using deconvolution and FOV
extension; and
FIG. 13 shows two magnetic resonance images produced using
compressed sensing and different CSM calculations.
DETAILED DESCRIPTION OF THE EMBODIMENTS
Like numbered elements in these figures are either equivalent
elements or perform the same function. Elements which have been
discussed previously will not necessarily be discussed in later
figures if the function is equivalent.
FIG. 1 shows an example of a magnetic resonance imaging system 100.
The magnetic resonance imaging system 100 comprises a magnet 104.
The magnet 104 is a superconducting cylindrical type magnet 104
with a bore 106 through it. The use of different types of magnets
is also possible for instance it is also possible to use both a
split cylindrical magnet and a so called open magnet. A split
cylindrical magnet is similar to a standard cylindrical magnet,
except that the cryostat has been split into two sections to allow
access to the iso-plane of the magnet, such magnets may for
instance be used in conjunction with charged particle beam therapy.
An open magnet has two magnet sections, one above the other with a
space in-between that is large enough to receive a subject: the
arrangement of the two sections area similar to that of a Helmholtz
coil. Open magnets are popular, because the subject is less
confined. Inside the cryostat of the cylindrical magnet there is a
collection of superconducting coils. Within the bore 106 of the
cylindrical magnet 104 there is an imaging zone 108 where the
magnetic field is strong and uniform enough to perform magnetic
resonance imaging.
Within the bore 106 of the magnet there is also a set of magnetic
field gradient coils 110 which is used for acquisition of magnetic
resonance data to spatially encode magnetic spins within the
imaging zone 108 of the magnet 104. The magnetic field gradient
coils 110 connected to a magnetic field gradient coil power supply
112. The magnetic field gradient coils 110 are intended to be
representative. Typically magnetic field gradient coils 110 contain
three separate sets of coils for spatially encoding in three
orthogonal spatial directions. A magnetic field gradient power
supply supplies current to the magnetic field gradient coils. The
current supplied to the magnetic field gradient coils 110 is
controlled as a function of time and may be ramped or pulsed.
Within the bore 106 of the magnet 104 is a body coil 114. The body
coil 114 may be a QBC. The body coil 114 is shown as being
connected to a transceiver 116. In some embodiments body coil 414
may also be connected to a whole body coil radio frequency
amplifier and/or receiver, however this is not shown in this
example. If both a transmitter and a receiver 116 are connected to
the whole body coil 114, a means for switching between the transmit
and receive mode may be provided. For example a circuit with a pin
diode may be used to select the transmit or receive mode. A subject
support 120 supports a subject 118 within the imaging zone.
A transceiver 122 is shown as being connected to a magnetic
resonance imaging coil 124. In this example the magnetic resonance
imaging coil 124 is a surface coil comprising multiple antenna
elements 126. The transceiver 122 is operable for sending and
receiving individual RF signals to the individual antenna elements
126. In this example the transceiver 116 and the transceiver 122
are shown as being separate units. However, in other examples the
units 116 and 122 could be combined.
The transceiver 116, the transceiver 122, and the magnetic field
gradient coil power supply 112 are shown as being connected to a
hardware interface 132 of a computer 130. The computer 130 is
further shown as containing a processor 133 which is operable for
executing the machine-readable instructions. The computer 130 is
further shown as comprising a user interface 134, computer storage
136 and computer memory 138 which are all accessible and connected
to the processor 133.
The computer storage 136 is shown as containing one of more pulse
sequences 140. The pulse sequences 140 are either instructions or
data which can be converted into instructions which enable the
processor 133 to acquire magnetic resonance data using the magnetic
resonance imaging system 100. The computer storage is further shown
as containing calibration magnetic resonance data 142 that was
acquired by the antenna elements 126. The computer storage is
further shown as containing modified magnetic resonance data 144.
The modified magnetic resonance data 144 was created by
interpolating the calibration magnetic resonance data 142. This may
be performed using several different techniques. It may be done
with an image-based method or directly through interpolation in
k-space.
The computer storage 136 is further shown as containing a set of
coil sensitivity kernels 146 that were calculated using at least
the modified magnetic resonance data 144. The coil sensitivity
kernels 146 may be calculated in a variety of different ways also.
They for instance may be calculated by using an image from a body
coil or by iteratively calculating a reference image.
The computer storage 136 is further shown as containing a set of
coil sensitivities 148 that were calculated from the coil
sensitivity kernels 146. The computer storage is further shown as
containing a first magnetic resonance image that was calculated for
each antenna element 26 using the calibration magnetic resonance
data 142. The computer storage 152 is further shown as containing a
modified magnetic resonance image also for each antenna element
126. The modified magnetic resonance image is an image that is
padded with zeros and then the first magnetic resonance image is
placed into it in the appropriate or correct location. In some
examples the first magnetic resonance image 150 and the modified
magnetic resonance image 152 are not present. In other examples the
modified magnetic resonance image 152 is transformed back into
Fourier space to provide the modified magnetic resonance data
144.
The computer storage is further showing a set of points in Fourier
space 154 which are used as points for interpolation. Element 154
is not present in all examples. However, in some examples these
sets of points in Fourier space 154 are used as the locations where
the calibration magnetic resonance data 142 is directly
interpolated to the modified magnetic resonance data 144.
In some examples the body coil 114 is not present. In other
examples the body coil 114 is used to acquire body coil magnetic
resonance data 156. The body coil magnetic resonance data 156 is
magnetic resonance data that has been acquired with the body coil.
The computer storage 136 is shown as containing modified body coil
magnetic resonance data 158. In some examples the modified body
coil magnetic resonance data 158 is used with the modified magnetic
resonance data 144 to calculate the coil sensitivity kernels 146.
In other examples an iterative approach is used.
If an iterative approach is used then the computer storage 136
shows a reference image 160. In some instances the reference image
160 and the intermediate coil sensitivity 162 are used in an
iterative approach which will be described below. The computer
storage 136 is also optionally shown as containing imaging magnetic
resonance data 164 and a diagnostic magnetic resonance image 166.
The imaging magnetic resonance data 164 may be acquired using a
parallel imaging magnetic resonance technique. The set of coil
sensitivities 148 are then used to reconstruct the diagnostic
magnetic resonance image 166 using the imaging magnetic resonance
data 164.
The computer memory 138 is shown as being a control module 170. The
control module 170 contains computer-executable code or
instructions which enable the processor 133 to control the
operation and function of the magnetic resonance imaging system.
For instance the control module 170 may work in conjunction with
the pulse sequences 140 to acquire the various magnetic resonance
data. The computer memory 138 is shown as further containing an
imaging reconstruction Fourier transform module 172 and a k-space
interpolation module 174. These two modules 172, 174 contain
computer-executable code which enable the processor 133 to perform
one or more of the methods shown in FIGS. 2 to 6.
FIG. 2 shows a flowchart which illustrates a method of operating
the magnetic resonance imaging system 100 shown in FIG. 1. First in
step 200 calibration magnetic resonance data 142 is acquired from a
first field of view for multiple antenna elements 126. Next in step
202 modified magnetic resonance data 144 is calculated by
interpolating the calibration magnetic resonance data to a second
field of view. Next in step 204 a coil sensitivity kernel 146 is
calculated 204 by deconvolving the modified magnetic resonance data
144. Next in step 206 a coil sensitivity 148 is calculated for each
of the multiple antenna elements by transforming each coil
sensitivity matrix into image space. Next in step 208 the size of
the coil sensitivities is optionally reduced by cropping each coil
sensitivity to the first field of view. Next in step 210 magnetic
resonance data 64 is optionally acquired. In step 212 a diagnostic
magnetic resonance image 166 is optionally reconstructed using the
imaging magnetic resonance data 164 and the set of coil
sensitivities 148. Steps 210 and 212 are a parallel imaging
technique that uses the determined set of coil sensitivities
148.
FIG. 3 shows a flowchart which explains one way in which step 202
of FIG. 2 could be performed. That is to say FIG. 3 shows a
flowchart which illustrates a method of calculating modified
magnetic resonance data by interpolating the calibration magnetic
resonance data to a second field of view. First in step 300 a first
magnetic resonance image 150 is reconstructed for each antenna
element using the calibration magnetic resonance data 142. Next in
step 302 a modified magnetic resonance image 152 is calculated by
pasting the first magnetic resonance image 150 into a null valued
image. Finally in step 304, the modified magnetic resonance data
144 is calculated by Fourier transforming the modified magnetic
resonance image 152 into Fourier space.
FIG. 4 shows an alternative way of performing step 202 of FIG. 2.
FIG. 4 is an alternative way of calculating modified magnetic
resonance data 144 by interpolating the calibration magnetic
resonance data 142 to a second field of view. In FIG. 400 there is
one step labeled 400. Step 400 is to interpolate the calibration
magnetic resonance data 142 to the modified magnetic resonance data
144 directly in k-space using the set of predefined points 154.
FIG. 5 shows one way of performing step 204 of FIG. 2. FIG. 5
illustrates one way of calculating a coil sensitivity kernel 146 by
deconvolving the modified magnetic resonance data 144. First in
step 500 body coil magnetic resonance data 156 is acquired from the
first field of view using the body coil 114. Next in step 502 the
body coil magnetic resonance data is interpolated to the second
field of view to calculate the modified body coil magnetic
resonance data 158. This interpolation may be performed in a manner
analogous to that shown in FIG. 3 or 4 that was used for the
calibration magnetic resonance data. Finally step 504 shows to
calculate the coil sensitivity kernel 146 by deconvolving the
modified magnetic resonance data 144 with respect to the modified
body coil magnetic resonance data 158.
FIG. 6 shows an iterative method of performing steps 204 and 206 of
FIG. 2. FIG. 6 shows an iterative method of calculating a coil
sensitivity kernel 146 by deconvolving the modified magnetic
resonance data 144. First in step 600 a reference image is set to a
predetermined value. The reference image is a predetermined image
with the second field of view. Next in step 602 an intermediate
coil sensitivity kernel 161 is calculated by deconvolving relative
to the reference image 160. Next in step 604 an intermediate coil
sensitivity 162 is calculated by transforming the intermediate coil
sensitivity kernel 161 to image space. In step 606 the reference
image 160 is recalculated using the intermediate coil sensitivities
162 and the calibration magnetic resonance data 142. Steps 602, 604
and 606 are performed iteratively where the method is repeated with
the new reference image 160. These three steps may be performed a
predetermined number of times or the new reference image may be
compared to the reference image of the previous iteration to see if
the answer has converged to within a statistical measure. This is
represented in box 608 which is a decision box with the question
has reference image converged, if no then the loop repeats back to
step 602. If it has then step 610 is performed and the intermediate
coil sensitivity is used as the coil sensitivity.
Accurate coil sensitivity maps (CSMs) are beneficial for parallel
MR imaging methods. Examples described herein may provide a method
to calculate CSMs by a deconvolution in k-space. The proposed
method may have one or more of the following benefits over known
methods:
Requires less data than image based CSM calculation. I.e., it
enables fast acquisition of CSM data.
More accurate CSM near edges of the object resulting in less
fold-over artifacts. This is also relevant in situations where
there are signal voids within the imaged object.
Less susceptible to noisy data.
Orders of magnitude faster than other k-space based methods (e.g.
ESPIRiT).
Coil sensitivity maps (CSMs) describe the spatial dependency of
receive sensitivity (phase and amplitude) of MRI receive coils.
Accurate knowledge of the CSMs is required for parallel MR imaging
methods to combine the signal of different receive coils into one
image and to enable accelerated MR imaging.
Currently, CSMs are calculated in image space by acquiring low
resolution images with each receive coil and dividing these images
by a common reference image (e.g. body coil or sum-of-squares
image).
An alternative to the image based CSM calculation could be ESPIRiT,
a k-space based method to calculate CSM as eigenvectors of a linear
system, which is currently only used in research applications.
However, this method is orders of magnitude slower than the image
based method (several minutes calculation time for a 2D CSM), which
makes it unsuitable for routine use.
One problem of calculating the CSM by division of two low
resolution images is that this approach has inherent systematic
errors. These errors are largest at edges of the imaged object.
These systematic errors can result in back-folding artifacts.
A second problem with the current approach is that it only leads to
a meaningful result in regions where the MR signal strength is
sufficient. Outside this region the quality of the CSM degrades
rapidly. This can cause problems if there are signal voids within
the imaged object, especially when iterative reconstruction
algorithms, e.g. compressed sensing, are used.
The image based CSM calculation requires a certain spatial
resolution and also requires high signal to noise ratio to give
valid results. Consequently, a considerable time is required to
acquire the CSM data.
Similar to ESPIRiT, the proposed method avoids the first two
problems by calculating the CSM in k-space. But it can be executed
orders of magnitude faster than ESPIRiT, resulting in a computation
time similar to the image based calculation.
In addition, the proposed method requires less data than the image
based method, allowing to reduce the acquisition time.
In examples, the CSM for each receive coil is represented by a
small kernel in k-space. The kernel is calculated for each channel
by deconvolution of the coil and the reference data in k-space. One
step for the proposed method is that before the deconvolution is
carried out, the field of view is extended in such a way that there
is sufficient empty space around the object.
Below is a detailed list comprising steps of a method to calculate
CSMs:
1. Acquire receive coil data and reference data. This is the same
as in the current approach to obtain CSMs (with the additional
option of acquiring less data than currently).
2. Transform to image space. Simple Fourier-Transform.
3. Extend field of view (Zero padding in image space).
Extending the field of view by zero padding is in principle a
trivial operation. However, the necessity for it may be not
obvious: The CSMs are to be represented by small kernels in
k-space. This is motivated by the smooth spatial variation of the
CSM. However, the CSM are monotonously decaying with increasing
distance to the coil. I.e. in general the CSM will have a high
value at one edge of the object and a low value at the opposite
edge of the object. If the object fills the field of view, which is
a typically the case in the phase-encoding direction, a discrete
representation of the CSM in k-space is not possible by low
frequency components alone, because discrete Fourier-transforms
represent cyclic functions and the strong jump which occurs at the
wrap-around at the image boundary contains large high frequency
components.
Extending the field of view by zero filling introduces an area into
the coil and reference images from which no information about the
coil sensitivity can be obtained. I.e. arbitrary fluctuations of
the CSM in this region are not in contradiction to the data. If the
field of view extension is sufficiently large (e.g. a factor of 2),
it enables connecting the high and low sensitivity regions using
only low frequency components. Thus the CSM can then be represented
by a small kernel.
4. Transform extended coil and reference images to k-space. Simple
Fourier-Transform.
5. Calculate CSM kernel by deconvolution in k-space (using the
extended data). Inversion of a small linear system (similar to
GRAPPA kernel calculation).
6. Transform kernel to image space. Simple Fourier-Transform.
7. Reduce CSM to original field of view. Simple truncation.
These steps are applied individually to each receive channel. I.e.,
the data of different receive channels are not mixed as e.g. in
ESPIRiT or GRAPPA. As a result, the equation system that needs to
be inverted in step 5 is small, enabling a fast calculation.
FIG. 7 shows an example of a one-dimensional object which is
mathematically used to show the advantage of using the examples of
methods described herein. The axis labeled 702 may be spatial
coordinates and the axis labeled 704 is the object magnetization.
It can be seen that there are several regions with zero
magnetization.
FIG. 8 shows the coil sensitivity 800 as a function of position 702
in several different methods. The solid line labeled 802 is the
true coil sensitivity. The solid line 804 shows the coil
sensitivity calculated using image division 804. The dashed line
shows the coil sensitivity using only deconvolution 806. The
dashed-dot line 808 shows deconvolution with the field of view
extensions or equivalently the interpolation in k-space from the
first to the second field of view. It can be seen that the
deconvolution of the field of view extensions 808 reproduces the
true coil sensitivity 802 much better than any of the others.
The errors of the calculated coil sensitivity with respect to the
true coil sensitivity 802 are shown in FIG. 9. The spatial
coordinates are axis 702 and the value of the calculated coil
sensitivity divided by the true coil sensitivity is shown in axis
labeled 900. Again the version using image division is labeled 804,
deconvolution is labeled 806 and the dashed-dot line is the
deconvolution with the field of view extension 808. It can be seen
that the deconvolution with field of view extension 808 is the most
accurate.
FIGS. 7, 8, and 9 show a comparison of CSMs calculated from
simulated, one-dimensional data with FIG. 7 image division, FIG. 8
deconvolution without field of view extension and FIG. 9 the method
comprising deconvolution with field of view extension.
FIG. 7 shows the assumed magnetization of the object, i.e. this
graph shows the signal strength that would be measured with a
homogeneous coil. It also shows that the object almost fills the
field of view and contains two signal voids.
FIG. 8 shows the values of the calculated coil sensitivities, and
the bottom graph shows the ratio of the calculated CSM and the true
CSM that was used in the simulation.
FIG. 9 demonstrates that the proposed method results in the most
accurate CSMs, it also demonstrates that the CSMs continue smoothly
through internal signal voids.
FIG. 10 shows an example of a magnetic resonance image that was
produced using the SENSE method using a 13 channel radio-frequency
system with 13 multiple antenna elements. The Fig. shown in FIG. 10
was reproduced using these well-known method of calculating coil
sensitivities using the division method of images. In this image
there can be seen a large number of artifacts labeled 1000.
FIG. 11 shows a magnetic resonance image calculated using the same
data as was used in FIG. 10. However, the coil sensitivities were
calculated differently. For FIG. 11 a kernel which is square and
had a size of 7.times.7 in k-space was used. It can be seen a
number of artifacts 1000 are still slightly visible in this Fig.
However, the artifacts are less pronounced and not as easy to see
in this image.
FIG. 12 shows a magnetic resonance image calculated using the same
data as for FIGS. 10 and 11. However, in this example a 15.times.15
kernel in k-space was used during the deconvolution. In this Fig.
no image artifacts are visible. In both FIGS. 11 and 12
interpolation of the calibration magnetic resonance data was used
for both. The difference is in the size of the kernel used for the
deconvolution.
FIG. 13 shows two FIGS. 1300 and 1302. The images 1300, 1302 were
reconstructed from the same data. However, different coil
sensitivities were used. The magnetic resonance data for these two
images 1300, 1302 was acquired using a compressed sensing magnetic
resonance technique. In image 1300 the normal image division method
of calculating the coil sensitivities was used. A number of
artifacts 1304 are visible in this image. For image 1302 the
calibration magnetic resonance data was interpolated to the
modified magnetic resonance data and then a coil sensitivity kernel
was calculated in order to calculate the coil sensitivities. In
FIG. 1302 none of the artifacts 1300 are visible.
A way of representing the coil sensitivities matrix in k-space can
be performed by using a discrete number of values on a grid where
the magnetization is known. This uses only the low-frequency
components and smoothes out small voids or holes in the object or
its magnetization.
The k-space grid of the acquired data is not used because it
enforces the periodicity of c.sub.i with the size of the field of
view. Instead the field of view is extended before deconvolution is
carried out. For example a factor of 2 larger may be used.
While the invention has been illustrated and described in detail in
the drawings and foregoing description, such illustration and
description are to be considered illustrative or exemplary and not
restrictive; the invention is not limited to the disclosed
embodiments.
Other variations to the disclosed embodiments can be understood and
effected by those skilled in the art in practicing the claimed
invention, from a study of the drawings, the disclosure, and the
appended claims. In the claims, the word "comprising" does not
exclude other elements or steps, and the indefinite article "a" or
"an" does not exclude a plurality. A single processor or other unit
may fulfill the functions of several items recited in the claims.
The mere fact that certain measures are recited in mutually
different dependent claims does not indicate that a combination of
these measured cannot be used to advantage. A computer program may
be stored/distributed on a suitable medium, such as an optical
storage medium or a solid-state medium supplied together with or as
part of other hardware, but may also be distributed in other forms,
such as via the Internet or other wired or wireless
telecommunication systems. Any reference signs in the claims should
not be construed as limiting the scope.
LIST OF REFERENCE NUMERALS
200 first item 100 magnetic resonance imaging system 104 magnet 106
bore of magnet 108 imaging zone 110 magnetic field gradient coils
112 magnetic field gradient coil power supply 114 body coil 116
transceiver 118 subject 120 subject support 122 transceiver 124
magnetic resonance image coil 126 antenna element 130 computer 132
hardware interface 134 user interface 136 computer storage 138
computer memory 140 pulse sequences 142 calibration magnetic
resonance data 144 modified magnetic resonance data 146 coil
sensitivity kernels 148 set of coil sensitivities 150 first
magnetic resonance image 152 modified magnetic resonance image 154
set of points in Fourier space 156 body coil magnetic resonance
data 158 modified body coil magnetic resonance data 160 reference
image 161 intermediate coil sensitivity kernel 162 intermediate
coil sensitivity 164 imaging magnetic resonance data 166 diagnostic
magnetic resonance image 170 control module 172 Image
reconstruction and Fourier transform module 174 k-space
interpolation module 700 1-D object 702 spatial coordinate 704
object magnetization 800 coil sensitivity 802 true coil sensitivity
804 coil sensitivity calculated using image division 806 coil
sensitivity using deconvolution in Fourier space 808 coil
sensitivity using FOV extension 900 coil sensitivity error 1000
artifact 1300 magnetic resonance image 1302 magnetic resonance
image 1304 artifact
* * * * *